PROJECT SUMMARY/ABSTRACT Unrelenting pathological fatigue is a common symptom across a wide range of diseases and is the prominent feature of Myalgic Encephalomyelitis / Chronic Fatigue Syndrome (ME/CFS). Despite its near ubiquitous reporting, little is known about the neurobiology of fatigue. ME/CFS diagnoses presents a definitive medical challenge. Misdiagnoses are common due to lack of biomarkers, reliance on a subjective phemonological criteria and symptom overlap with depression. Individuals with ME/CFS also present with complaints of executive dysfunction. Although cross sectional fMRI studies have reported unique changes in resting state and task evoked brain activity, findings are not specific and show overlap with depression. A striking clinical feature of ME/CFS is the response to exercise, known as post-exertional malaise (PEM). PEM is recognized as the incremental increase in fatigue severity and executive dysfunction following acute exercise. In contrast, patients with depression report a decrease in symptom burden. Despite this known divergent response, studies trying to illuminate predictive biomarkers or the causative association between exercise and changes in functional brain activity have not been adequately attempted. To address this gap, we developed and subsequently tested the utility of a novel and longitudinal 3-day fMRI-exercise paradigm on Gulf War Illness (GWI); a disorder with tremendous overlap of symptoms. Published findings and preliminary evidence from our GWI studies suggest our paradigm may be effective in revealing the neurobiology of fatigue in ME/CFS. This proposal aims to harness the unique combination of fMRI and exercise to elucidate neural correlates of fatigue in ME/CFS. The proposal has three aims. The first aim is to characterize exercise associated decrements in fatigue by modeling changes in functional connectivity within the Default Mode Network. The second aim is designed to demonstrate the consequence of exercise on the causal interactions between higher level cognitive neural systems by effective connectivity analysis. The third aim will leverage machine learning algorithms to predict ME/CFS status from baseline task-free resting state fMRI. Findings would provide strong evidence of pathophysiological mechanisms that maintain the chronicity of fatigue in ME/CFS. Furthermore, the importance of an accurate and detailed description of the neurobiological constructs of fatigue will be a critical step in developing reliable diagnostic tests and potential therapeutic targets. Outcomes from this proposal would suggest an alternative paradigm for studying the neurobiology of fatigue in a broad number of pathological states and potential detection of this almost universal symptom complaint in a clinical setting.